1. Field of the Invention
The present invention relates to a discriminant model learning device, a discriminant model learning method and a discriminant model learning program for learning a discriminant model for discriminating data.
2. Description of the Related Art
An important industrial object is to efficiently process a large scale and large amount of data along with recent rapid development of data infrastructure. Particularly, a technique for discriminating which category data belongs to is one of main techniques in many applications such as data mining and pattern recognition.
An example utilizing a data discriminating technique is to make predictions on unclassified data. For example, when a vehicle failure diagnosis is made, sensor data obtained from the vehicle and past failure cases are learned thereby to generate a rule for discriminating failures. Then, the generated rule is applied to the sensor data of the vehicle in which a new failure has occurred (that is, unclassified data), thereby specifying a failure occurring in the vehicle or narrowing (predicting) its causes.
The data discriminating technique is also used for analyzing a difference between categories or factors. For example, when a relationship between a disease and a lifestyle is to be examined, a group to be examined is classified into a group having a disease and a group not having the same, and a rule for discriminating the two groups is only learned. For example, the thus-learned rule is assumed to be “when an object person is obese and a smoker, he/she has a high possibility of a disease.” In this case, if both the conditions of “obese” and “smoker” are met, they are suspicious of important factors of the disease.
For the problem on data discrimination, the most important object is how to learn a discriminant model indicating a rule for classifying data from target data. Thus, there are proposed many methods for learning a discriminant model from data which is given with category information based on past cases or simulation data. The methods are learning methods using a discriminant label, and are called “supervised learning.” The category information may be denoted as discriminant label in the following. NPTL 1 describes therein exemplary supervised learning such as logistic regression, support vector machine and decision tree.
NPTL 2 describes therein a semi-supervised learning method which supposes a distribution of discriminant labels and makes use of data without discriminant label. NPTL 2 describes therein a Laplacian support vector machine as exemplary semi-supervised learning.
NPLT 3 describes therein a technique called covariate shift or domain adaptation for performing discrimination learning in consideration of a change in data nature.
NPLT 4 describes therein uncertainty which data necessary for learning a discriminant model gives to estimation of a model.